Comparative study of disease progression for heart failure with different etiologies via time-ordered network analysis.

ABSTRACT

OBJECTIVES: Heart failure (HF), the primary end-stage manifestation of multiple cardiovascular diseases, has become a global epidemic with high morbidity and mortality. However, the mechanisms underlying the pathogenesis of HF with different etiologies have yet to be fully elucidated. METHODS: In this study, we developed a novel method to determine the dysregulated lncRNA-mRNA regulation pairs (LMRPs) in the different causes that lead to HF. Time-ordered dysregulated lncRNA-mRNA regulation networks were constructed for comparing the HF progression initiated from different causes. Additionally, the random forest and support vector machine classification algorithm were applied to identify HF-related diagnostic biomarkers. RESULTS: Biological functional analysis indicated that similar functions were detected at the late stage across different causes of HF, whereas different characteristics were revealed during disease progression. Specifically, the disturbance of myocardial energy metabolism might be a cause of dilated cardiomyopathy (DCM) and peripartum cardiomyopathy (PPCM), while immune response appeared earlier in hypertrophic cardiomyopathy (HCM). Inflammatory response during HCM and PPCM progression might be mediated by complement system, whereas ischemic cardiomyopathy (ICM) might be induced by cytokines. Finally, we identified several panels of diagnostic biomarkers for distinguishing HF patients of different etiologies from non-heart failure (NF) controls. CONCLUSIONS: This study revealed distinct functional characteristics during the progression of HF from different causes and facilitated the discovery of candidate diagnostic biomarkers for HF.